A gap-filling algorithm selection strategy for GRACE and GRACE Follow-On time series based on hydrological signal characteristics of the individual river basins

IF 0.9 Q4 REMOTE SENSING
Hamed Karimi, S. Iran-Pour, A. Amiri-Simkooei, M. Babadi
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引用次数: 0

Abstract

Abstract Gravity recovery and climate experiment (GRACE) and GRACE Follow-On (GRACE-FO) are Earth’s gravity satellite missions with hydrological monitoring applications. However, caused by measuring instrumental problems, there are several temporal missing values in the dataset of the two missions where a long gap between the mission dataset also exists. Recent studies utilized different gap-filling methodologies to fill those data gaps. In this article, we employ a variety of singular spectrum analysis (SSA) algorithms as well as the least squares-harmonic estimation (LS-HE) approach for the data gap-filling. These methods are implemented on six hydrological basins, where the performance of the algorithms is validated for different artificial gap scenarios. Our results indicate that each hydrological basin has its special behaviour. LS-HE outperforms the other algorithms in half of the basins, whereas in the other half, SSA provides a better performance. This highlights the importance of different factors affecting the deterministic signals and stochastic characteristics of climatological time series. To fill the missing values of such time series, it is therefore required to investigate the time series behaviour on their time-invariant and time-varying characteristics before processing the series.
基于单个流域水文信号特征的GRACE和GRACE Follow-On时间序列补空算法选择策略
重力恢复与气候实验(GRACE)和GRACE后续卫星(GRACE- fo)是具有水文监测应用的地球重力卫星任务。然而,由于测量仪器的问题,两个任务的数据集中存在多个时间缺失值,并且任务数据集之间也存在较大的差距。最近的研究利用不同的空白填补方法来填补这些数据空白。在本文中,我们采用了各种奇异谱分析(SSA)算法以及最小二乘谐波估计(LS-HE)方法来填充数据间隙。这些方法在六个水文流域实施,在不同的人工间隙情景下验证了算法的性能。我们的研究结果表明,每个水文流域都有其特殊的行为。LS-HE在一半的流域中表现优于其他算法,而在另一半流域中,SSA提供了更好的性能。这突出了影响气候时间序列确定性信号和随机特征的不同因素的重要性。因此,为了填补这些时间序列的缺失值,需要在处理序列之前研究时间序列的时不变和时变特征。
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来源期刊
Journal of Geodetic Science
Journal of Geodetic Science REMOTE SENSING-
CiteScore
1.90
自引率
7.70%
发文量
3
审稿时长
14 weeks
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